Overview

Dataset statistics

Number of variables12
Number of observations5695
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory534.0 KiB
Average record size in memory96.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly correlated with qty_invoice_no and 3 other fieldsHigh correlation
recency_days is highly correlated with qty_invoice_noHigh correlation
qty_invoice_no is highly correlated with gross_revenue and 5 other fieldsHigh correlation
qty_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qty_products is highly correlated with gross_revenue and 4 other fieldsHigh correlation
frequency is highly correlated with qty_invoice_no and 1 other fieldsHigh correlation
qty_returns is highly correlated with qty_invoice_noHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with qty_products and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qty_invoice_no and 1 other fieldsHigh correlation
qty_invoice_no is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qty_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
qty_products is highly correlated with qty_invoice_noHigh correlation
avg_ticket is highly correlated with qty_returns and 1 other fieldsHigh correlation
qty_returns is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
df_index is highly correlated with qty_invoice_no and 1 other fieldsHigh correlation
customer_id is highly correlated with qty_invoice_no and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qty_items and 3 other fieldsHigh correlation
recency_days is highly correlated with qty_invoice_no and 1 other fieldsHigh correlation
qty_invoice_no is highly correlated with df_index and 3 other fieldsHigh correlation
qty_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qty_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
frequency is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qty_returns is highly correlated with df_index and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with qty_productsHigh correlation
df_index is highly correlated with customer_id and 1 other fieldsHigh correlation
customer_id is highly correlated with df_index and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qty_invoice_no and 2 other fieldsHigh correlation
recency_days is highly correlated with df_index and 1 other fieldsHigh correlation
qty_invoice_no is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qty_items is highly correlated with gross_revenue and 5 other fieldsHigh correlation
qty_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with qty_items and 2 other fieldsHigh correlation
qty_returns is highly correlated with qty_items and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with qty_items and 2 other fieldsHigh correlation
gross_revenue is highly skewed (γ1 = 22.58539738) Skewed
qty_items is highly skewed (γ1 = 24.06874032) Skewed
avg_ticket is highly skewed (γ1 = 71.30308417) Skewed
qty_returns is highly skewed (γ1 = 71.05769911) Skewed
avg_basket_size is highly skewed (γ1 = 58.3280441) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
customer_id has unique values Unique
qty_returns has 4191 (73.6%) zeros Zeros

Reproduction

Analysis started2022-03-12 18:17:23.943312
Analysis finished2022-03-12 18:17:39.839252
Duration15.9 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct5695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2895.799122
Minimum0
Maximum5785
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:39.907890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile289.7
Q11454.5
median2898
Q34340.5
95-th percentile5494.3
Maximum5785
Range5785
Interquartile range (IQR)2886

Descriptive statistics

Standard deviation1668.934084
Coefficient of variation (CV)0.57632937
Kurtosis-1.196239242
Mean2895.799122
Median Absolute Deviation (MAD)1443
Skewness-0.003571604469
Sum16491576
Variance2785340.976
MonotonicityStrictly increasing
2022-03-12T15:17:39.995660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
38891
 
< 0.1%
38651
 
< 0.1%
38641
 
< 0.1%
38631
 
< 0.1%
38621
 
< 0.1%
38611
 
< 0.1%
38601
 
< 0.1%
38591
 
< 0.1%
38581
 
< 0.1%
Other values (5685)5685
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57851
< 0.1%
57841
< 0.1%
57831
< 0.1%
57821
< 0.1%
57811
< 0.1%
57801
< 0.1%
57791
< 0.1%
57781
< 0.1%
57771
< 0.1%
57761
< 0.1%

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct5695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16600.59895
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:40.088259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12699.1
Q114288.5
median16227
Q318210.5
95-th percentile21731.1
Maximum22709
Range10363
Interquartile range (IQR)3922

Descriptive statistics

Standard deviation2808.241892
Coefficient of variation (CV)0.1691650946
Kurtosis-0.8211159178
Mean16600.59895
Median Absolute Deviation (MAD)1963
Skewness0.4412705454
Sum94540411
Variance7886222.521
MonotonicityNot monotonic
2022-03-12T15:17:40.410123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
211101
 
< 0.1%
137451
 
< 0.1%
155841
 
< 0.1%
210891
 
< 0.1%
210881
 
< 0.1%
210871
 
< 0.1%
210861
 
< 0.1%
155781
 
< 0.1%
124241
 
< 0.1%
Other values (5685)5685
99.8%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
ValueCountFrequency (%)
227091
< 0.1%
227081
< 0.1%
227071
< 0.1%
227061
< 0.1%
227051
< 0.1%
227041
< 0.1%
227001
< 0.1%
226991
< 0.1%
226961
< 0.1%
226951
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5449
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1774.303486
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:40.501288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.171
Q1236.135
median613.2
Q31570.74
95-th percentile5309.696
Maximum279138.02
Range279137.6
Interquartile range (IQR)1334.605

Descriptive statistics

Standard deviation7582.209661
Coefficient of variation (CV)4.273344286
Kurtosis675.6156928
Mean1774.303486
Median Absolute Deviation (MAD)479.19
Skewness22.58539738
Sum10104658.35
Variance57489903.34
MonotonicityNot monotonic
2022-03-12T15:17:40.583854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.959
 
0.2%
2.958
 
0.1%
4.958
 
0.1%
1.258
 
0.1%
3.757
 
0.1%
12.757
 
0.1%
1.657
 
0.1%
5.956
 
0.1%
7.56
 
0.1%
4.256
 
0.1%
Other values (5439)5623
98.7%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
0.1%
1.441
 
< 0.1%
1.657
0.1%
1.691
 
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
77183.61
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.9276558
Minimum0
Maximum373
Zeros37
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:40.672888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median71
Q3200
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)177

Descriptive statistics

Standard deviation111.6463634
Coefficient of variation (CV)0.9548328206
Kurtosis-0.6426259548
Mean116.9276558
Median Absolute Deviation (MAD)61
Skewness0.8143144842
Sum665903
Variance12464.91047
MonotonicityNot monotonic
2022-03-12T15:17:40.760174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1110
 
1.9%
4105
 
1.8%
398
 
1.7%
292
 
1.6%
1086
 
1.5%
882
 
1.4%
1779
 
1.4%
979
 
1.4%
778
 
1.4%
1566
 
1.2%
Other values (294)4820
84.6%
ValueCountFrequency (%)
037
 
0.6%
1110
1.9%
292
1.6%
398
1.7%
4105
1.8%
552
0.9%
778
1.4%
882
1.4%
979
1.4%
1086
1.5%
ValueCountFrequency (%)
37323
0.4%
37223
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36615
0.3%
36519
0.3%
36411
0.2%
3627
 
0.1%

qty_invoice_no
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.471290606
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:40.854189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.813294304
Coefficient of variation (CV)1.962755378
Kurtosis302.0907206
Mean3.471290606
Median Absolute Deviation (MAD)0
Skewness13.19278315
Sum19769
Variance46.42097928
MonotonicityNot monotonic
2022-03-12T15:17:40.939296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12871
50.4%
2825
 
14.5%
3503
 
8.8%
4394
 
6.9%
5237
 
4.2%
6173
 
3.0%
7138
 
2.4%
898
 
1.7%
969
 
1.2%
1055
 
1.0%
Other values (46)332
 
5.8%
ValueCountFrequency (%)
12871
50.4%
2825
 
14.5%
3503
 
8.8%
4394
 
6.9%
5237
 
4.2%
6173
 
3.0%
7138
 
2.4%
898
 
1.7%
969
 
1.2%
1055
 
1.0%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
< 0.1%
861
< 0.1%
721
< 0.1%
622
< 0.1%
601
< 0.1%
571
< 0.1%

qty_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1840
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean964.4258121
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:41.027388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q1106
median317
Q3804.5
95-th percentile2931.8
Maximum196844
Range196843
Interquartile range (IQR)698.5

Descriptive statistics

Standard deviation4300.208161
Coefficient of variation (CV)4.458827322
Kurtosis863.3991637
Mean964.4258121
Median Absolute Deviation (MAD)253
Skewness24.06874032
Sum5492405
Variance18491790.23
MonotonicityNot monotonic
2022-03-12T15:17:41.116222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114
 
2.0%
273
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1225
 
0.4%
8822
 
0.4%
7221
 
0.4%
720
 
0.4%
Other values (1830)5256
92.3%
ValueCountFrequency (%)
1114
2.0%
273
1.3%
351
0.9%
449
0.9%
535
 
0.6%
629
 
0.5%
720
 
0.4%
818
 
0.3%
97
 
0.1%
1017
 
0.3%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
742151
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%

qty_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct529
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.609482
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:41.211546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median41
Q3106
95-th percentile332.3
Maximum7838
Range7837
Interquartile range (IQR)92

Descriptive statistics

Standard deviation210.5785979
Coefficient of variation (CV)2.273834097
Kurtosis510.2942936
Mean92.609482
Median Absolute Deviation (MAD)33
Skewness17.75339398
Sum527411
Variance44343.34589
MonotonicityNot monotonic
2022-03-12T15:17:41.303062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1256
 
4.5%
2149
 
2.6%
3108
 
1.9%
10101
 
1.8%
699
 
1.7%
992
 
1.6%
591
 
1.6%
487
 
1.5%
1183
 
1.5%
783
 
1.5%
Other values (519)4546
79.8%
ValueCountFrequency (%)
1256
4.5%
2149
2.6%
3108
1.9%
487
 
1.5%
591
 
1.6%
699
 
1.7%
783
 
1.5%
881
 
1.4%
992
 
1.6%
10101
 
1.8%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1226
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5472966614
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:41.396025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.01104363016
Q10.02492643315
median1
Q31
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.9750735668

Descriptive statistics

Standard deviation0.5502573935
Coefficient of variation (CV)1.005409739
Kurtosis139.1369501
Mean0.5472966614
Median Absolute Deviation (MAD)0
Skewness4.858644853
Sum3116.854487
Variance0.3027831991
MonotonicityNot monotonic
2022-03-12T15:17:41.484535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12879
50.6%
247
 
0.8%
0.062518
 
0.3%
0.0277777777817
 
0.3%
0.0238095238116
 
0.3%
0.0833333333315
 
0.3%
0.0909090909115
 
0.3%
0.0344827586214
 
0.2%
0.0294117647114
 
0.2%
0.0357142857113
 
0.2%
Other values (1216)2647
46.5%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
< 0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
247
 
0.8%
1.1428571431
 
< 0.1%
12879
50.6%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5502
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.74707736
Minimum0.42
Maximum77183.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:41.578392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile3.460023492
Q17.95
median15.85416667
Q321.98419438
95-th percentile76.32
Maximum77183.6
Range77183.18
Interquartile range (IQR)14.03419438

Descriptive statistics

Standard deviation1043.79839
Coefficient of variation (CV)23.32662716
Kurtosis5245.740162
Mean44.74707736
Median Absolute Deviation (MAD)7.495745614
Skewness71.30308417
Sum254834.6056
Variance1089515.079
MonotonicityNot monotonic
2022-03-12T15:17:41.667344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7511
 
0.2%
4.9510
 
0.2%
2.959
 
0.2%
1.259
 
0.2%
7.958
 
0.1%
12.757
 
0.1%
1.657
 
0.1%
8.257
 
0.1%
3.356
 
0.1%
156
 
0.1%
Other values (5492)5615
98.6%
ValueCountFrequency (%)
0.423
0.1%
0.5351
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.83714285711
 
< 0.1%
0.842
< 0.1%
0.853
0.1%
1.0022222221
 
< 0.1%
1.021
 
< 0.1%
1.038751
 
< 0.1%
ValueCountFrequency (%)
77183.61
< 0.1%
13305.51
< 0.1%
4453.431
< 0.1%
38611
< 0.1%
3202.921
< 0.1%
30961
< 0.1%
1687.21
< 0.1%
1377.0777781
< 0.1%
1001.21
< 0.1%
952.98751
< 0.1%

qty_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.26830553
Minimum0
Maximum74215
Zeros4191
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:41.761581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile38.3
Maximum74215
Range74215
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1004.321707
Coefficient of variation (CV)32.11947976
Kurtosis5232.359476
Mean31.26830553
Median Absolute Deviation (MAD)0
Skewness71.05769911
Sum178073
Variance1008662.091
MonotonicityNot monotonic
2022-03-12T15:17:41.849014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04191
73.6%
1169
 
3.0%
2150
 
2.6%
3105
 
1.8%
489
 
1.6%
678
 
1.4%
561
 
1.1%
1252
 
0.9%
744
 
0.8%
843
 
0.8%
Other values (204)713
 
12.5%
ValueCountFrequency (%)
04191
73.6%
1169
 
3.0%
2150
 
2.6%
3105
 
1.8%
489
 
1.6%
561
 
1.1%
678
 
1.4%
744
 
0.8%
843
 
0.8%
941
 
0.7%
ValueCountFrequency (%)
742151
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2369
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261.1135614
Minimum1
Maximum74215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:41.941650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q175
median151.6666667
Q3290.3125
95-th percentile733.5625
Maximum74215
Range74214
Interquartile range (IQR)215.3125

Descriptive statistics

Standard deviation1074.145537
Coefficient of variation (CV)4.113710262
Kurtosis3957.958196
Mean261.1135614
Median Absolute Deviation (MAD)96.66666667
Skewness58.3280441
Sum1487041.732
Variance1153788.635
MonotonicityNot monotonic
2022-03-12T15:17:42.039708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1115
 
2.0%
272
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1226
 
0.5%
10022
 
0.4%
7222
 
0.4%
7321
 
0.4%
Other values (2359)5253
92.2%
ValueCountFrequency (%)
1115
2.0%
272
1.3%
351
0.9%
3.3333333331
 
< 0.1%
449
0.9%
535
 
0.6%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
629
 
0.5%
6.1428571431
 
< 0.1%
ValueCountFrequency (%)
742151
< 0.1%
141491
< 0.1%
139561
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
59631
< 0.1%
51971
< 0.1%
43001
< 0.1%
42821
< 0.1%
42801
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1171
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.2557656
Minimum0.2
Maximum1109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-03-12T15:17:42.143770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1
Q17.25
median15
Q331
95-th percentile173
Maximum1109
Range1108.8
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation76.88286317
Coefficient of variation (CV)2.063650067
Kurtosis32.88891715
Mean37.2557656
Median Absolute Deviation (MAD)10
Skewness5.07356894
Sum212171.5851
Variance5910.97465
MonotonicityNot monotonic
2022-03-12T15:17:42.232330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1278
 
4.9%
2161
 
2.8%
3115
 
2.0%
10105
 
1.8%
9105
 
1.8%
8103
 
1.8%
5101
 
1.8%
7101
 
1.8%
6101
 
1.8%
1397
 
1.7%
Other values (1161)4428
77.8%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333337
0.1%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.2%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
11091
< 0.1%
7481
< 0.1%
7301
< 0.1%
7201
< 0.1%
7031
< 0.1%
6861
< 0.1%
6751
< 0.1%
6731
< 0.1%
6601
< 0.1%
6491
< 0.1%

Interactions

2022-03-12T15:17:38.634691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:27.294973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.302814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.269119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:30.255217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.468096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.440845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.451930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.442073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:35.637372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.647484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.668018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.710040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:27.408192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.381286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.350087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:30.338313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.542944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.520843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.534614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.528099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:35.716626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.728297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.748036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.784384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:27.486008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.458818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.427217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:30.418198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.615847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.598790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.614782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.604643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:35.795811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.806488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.826009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.858682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:27.561605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.533229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.501937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:30.495992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.688280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.678437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.694372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.679846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:35.873367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.912514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.903433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.936035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:27.641296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.613519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.624702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:30.791229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.764788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.761388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.778643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.763304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:35.956465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.006592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.984610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:39.006802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:27.718855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.687739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.697965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:30.864302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.838896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.836149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.854319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.838490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.035159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.080659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.059273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:39.087793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:27.799502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.772178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.776932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:30.967465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.949959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.921111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.939209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.924587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.127479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.164507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.142151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:39.167400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:27.888598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.854976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.855997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.052138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.058888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.007976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.024277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:35.207702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.215193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.248888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.225737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:39.272017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:27.971436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.934951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.933946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.133765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.135438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.094783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.107981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:35.292557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.298380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.334535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.310445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:39.356495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.053309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.028464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:30.013775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.217360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.212725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.189558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.192874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:35.379971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.383626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.425906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.395368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:39.434510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.134526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.113626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:30.091642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.300504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.292819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.287989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.274879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:35.466636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.466180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.509047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.477248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:39.512428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:28.220300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:29.194022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:30.176774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:31.388722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:32.369333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:33.371656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:34.361387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:35.556532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:36.547962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:37.590151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-12T15:17:38.557941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-12T15:17:42.312376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-12T15:17:42.437112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-12T15:17:42.552438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-12T15:17:42.662732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-12T15:17:39.633982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-12T15:17:39.781712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqty_invoice_noqty_itemsqty_productsfrequencyavg_ticketqty_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.017.00000018.15222240.050.9705880.617647
11130473232.5956.09.01390.0171.00.02830218.90403535.0154.44444411.666667
22125836705.382.015.05028.0232.00.04032328.90250050.0335.2000007.600000
3313748948.2595.05.0439.028.00.01792133.8660710.087.8000004.800000
4415100876.00333.03.080.03.00.073171292.00000022.026.6666670.333333
55152914623.3025.014.02102.0102.00.04011545.32647129.0150.1428574.357143
66146885630.877.021.03621.0327.00.05722117.219786399.0172.4285717.047619
77178095411.9116.012.02057.061.00.03352088.71983641.0171.4166673.833333
881531160767.900.091.038194.02379.00.24331625.543464474.0419.7142866.230769
99160982005.6387.07.0613.067.00.02439029.9347760.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqty_invoice_noqty_itemsqty_productsfrequencyavg_ticketqty_returnsavg_basket_sizeavg_unique_basket_size
56855776227004839.421.01.01074.062.01.078.0551610.01074.055.0
5686577713298360.001.01.096.02.01.0180.0000000.096.02.0
5687577814569227.391.01.079.012.01.018.9491670.079.010.0
568857792270417.901.01.014.07.01.02.5571430.014.07.0
56895780227053.351.01.02.02.01.01.6750000.02.02.0
56905781227065699.001.01.01747.0634.01.08.9889590.01747.0634.0
56915782227076756.060.01.02010.0730.01.09.2548770.02010.0730.0
56925783227083217.200.01.0654.059.01.054.5288140.0654.056.0
56935784227093950.720.01.0731.0217.01.018.2060830.0731.0217.0
5694578512713794.550.01.0505.037.01.021.4743240.0505.037.0